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Title: A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

Abstract

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertainty and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.

Authors:
 [1];  [2];  [2];  [1]
  1. Univ. of Texas, Richardson, TX (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1457667
Report Number(s):
NREL/JA-5D00-70233
Journal ID: ISSN 1949-3053
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: none; Journal Issue: none; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 42 ENGINEERING; conditional probabilistic forecast; copula theory; Gaussian mixture model; wind power ramps

Citation Formats

Cui, Mingjian, Krishnan, Venkat, Hodge, Bri -Mathias, and Zhang, Jie. A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps. United States: N. p., 2018. Web. doi:10.1109/TSG.2018.2841932.
Cui, Mingjian, Krishnan, Venkat, Hodge, Bri -Mathias, & Zhang, Jie. A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps. United States. https://doi.org/10.1109/TSG.2018.2841932
Cui, Mingjian, Krishnan, Venkat, Hodge, Bri -Mathias, and Zhang, Jie. Tue . "A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps". United States. https://doi.org/10.1109/TSG.2018.2841932. https://www.osti.gov/servlets/purl/1457667.
@article{osti_1457667,
title = {A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps},
author = {Cui, Mingjian and Krishnan, Venkat and Hodge, Bri -Mathias and Zhang, Jie},
abstractNote = {Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertainty and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.},
doi = {10.1109/TSG.2018.2841932},
url = {https://www.osti.gov/biblio/1457667}, journal = {IEEE Transactions on Smart Grid},
issn = {1949-3053},
number = none,
volume = none,
place = {United States},
year = {2018},
month = {5}
}

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